






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase        =							logging.get_logger(__name__)

__lowerCAmelCase        =							{
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Optional[int]      			=     '''open-llama'''







  def __init__(			self   ,	UpperCamelCase__=100_000   ,	UpperCamelCase__=4_096   ,	UpperCamelCase__=11_008   ,	UpperCamelCase__=32   ,	UpperCamelCase__=32   ,	UpperCamelCase__="silu"   ,	UpperCamelCase__=2_048   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=1e-6   ,	UpperCamelCase__=True   ,	UpperCamelCase__=0   ,	UpperCamelCase__=1   ,	UpperCamelCase__=2   ,	UpperCamelCase__=False   ,	UpperCamelCase__=True   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			vocab_size
         a_						     =			max_position_embeddings
         a_						     =			hidden_size
         a_						     =			intermediate_size
         a_						     =			num_hidden_layers
         a_						     =			num_attention_heads
         a_						     =			hidden_act
         a_						     =			initializer_range
         a_						     =			rms_norm_eps
         a_						     =			use_cache
         a_						     =			kwargs.pop(
             'use_memorry_efficient_attention'   ,	UpperCamelCase__	)
         a_						     =			hidden_dropout_prob
         a_						     =			attention_dropout_prob
         a_						     =			use_stable_embedding
         a_						     =			shared_input_output_embedding
         a_						     =			rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=UpperCamelCase__   ,	bos_token_id=UpperCamelCase__   ,	eos_token_id=UpperCamelCase__   ,	tie_word_embeddings=UpperCamelCase__   ,	**UpperCamelCase__   ,	)







  def    _a       (			self	):






         """simple docstring"""


         if self.rope_scaling is None:
                return

         if not isinstance(self.rope_scaling   ,	UpperCamelCase__	) or len(self.rope_scaling	) != 2:
                raise ValueError(
                    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                    f'got {self.rope_scaling}'	)
         a_						     =			self.rope_scaling.get('type'   ,	UpperCamelCase__	)
         a_						     =			self.rope_scaling.get('factor'   ,	UpperCamelCase__	)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                raise ValueError(
                    f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'	)
         if rope_scaling_factor is None or not isinstance(UpperCamelCase__   ,	UpperCamelCase__	) or rope_scaling_factor <= 1.0:
                raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'	)












'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger("transformers.models.speecht5")


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Any   ):
       """simple docstring"""




       hf_model.apply_weight_norm()

       a_						     =			checkpoint['input_conv.weight_g']
       a_						     =			checkpoint['input_conv.weight_v']
       a_						     =			checkpoint['input_conv.bias']

       for i in range(len(config.upsample_rates   )   ):
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_g']
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_v']
              a_						     =			checkpoint[F'upsamples.{i}.1.bias']

       for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
              for j in range(len(config.resblock_dilation_sizes   )   ):
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       a_						     =			checkpoint['output_conv.1.weight_g']
       a_						     =			checkpoint['output_conv.1.weight_v']
       a_						     =			checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  List[str]=None   ,       lowercase_       :  Tuple=None   ,       ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			SpeechTaHifiGanConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			SpeechTaHifiGanConfig()

       a_						     =			SpeechTaHifiGan(lowercase_   )

       a_						     =			torch.load(lowercase_   )
       load_weights(orig_checkpoint['model']['generator']   ,       lowercase_   ,       lowercase_   )

       a_						     =			np.load(lowercase_   )
       a_						     =			stats[0].reshape(-1   )
       a_						     =			stats[1].reshape(-1   )
       a_						     =			torch.from_numpy(lowercase_   ).float()
       a_						     =			torch.from_numpy(lowercase_   ).float()

       model.save_pretrained(lowercase_   )

       if repo_id:
              print('Pushing to the hub...'   )
              model.push_to_hub(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
  parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )




